Analytical Chemistry
Analytical Chemistry
5 years ago

Analysis of Liquid Chromatography–Mass Spectrometry Data with an Elastic Net Multivariate Curve Resolution Strategy for Sparse Spectral Recovery

Analysis of Liquid Chromatography–Mass Spectrometry Data with an Elastic Net Multivariate Curve Resolution Strategy for Sparse Spectral Recovery
Daniel W. Cook, Sarah C. Rutan
Analysis of liquid chromatography–mass spectrometry (LC-MS) data requires the differentiation between a small number of relevant chemical signals and a larger amount of noise. This is often done based, at least partially, on a threshold which assumes that low intensity m/z signals arise from the noise. This eliminates low-intensity fragments, isotopes, and adducts and may exclude relevant low-intensity compounds all together. This work describes the use of multivariate curve resolution–alternating least-squares with an additional sparse regression step using elastic net (MCR-ENALS) to distinguish relevant m/z signals without the use of a harsh thresholding step, thus allowing for discovery of low-intensity m/z signals corresponding to the analytes. This strategy is demonstrated first on a unit mass analysis of amphetamines in which relevant m/z signals are found at as low as a 0.1% intensity relative to the molecular m/z peak. The incorporation of MCR-ENALS into our previously reported data reduction strategy for analysis of high-resolution LC-MS is also demonstrated. Analysis based on only 0.3% of the original data set, while retaining low-intensity isotope peaks, was accomplished without the use of thresholding, allowing for the application of MCR-ENALS to the high-resolution LC-MS data.

Publisher URL: http://dx.doi.org/10.1021/acs.analchem.7b01832

DOI: 10.1021/acs.analchem.7b01832

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